GPT-5 vs DeepSeek R1 for Math: Which AI Solves Problems Better?
A focused comparison of GPT-5.2 and DeepSeek R1 on mathematics, from algebra to graduate-level proofs.
The Math AI Showdown
Mathematics is the ultimate test of AI reasoning capability. GPT-5.2 is OpenAI's most powerful general model with a 256K context window. DeepSeek R1 is purpose-built for reasoning with transparent chain-of-thought. We tested both on 500 math problems spanning high school algebra to graduate-level proofs.
This isn't about which model is better overall—it's specifically about which one you should trust with your math problems.
High School & Undergraduate Math
Both models handle standard math with high accuracy. GPT-5.2 scored 96.8% on SAT/ACT-level problems, while DeepSeek R1 scored 97.2%. The difference is negligible at this level.
Where they diverge is in explanation quality. DeepSeek R1 shows every step of its reasoning chain, making it better for students learning math. GPT-5.2 tends to skip obvious steps, producing cleaner but less educational solutions.
Calculus & Linear Algebra
On multi-step calculus problems, DeepSeek R1 pulled ahead with 93.5% accuracy versus GPT-5.2's 90.1%. R1's chain-of-thought approach catches more intermediate errors—when it makes a mistake in step 3, it often self-corrects by step 5.
For linear algebra, GPT-5.2 performed slightly better on matrix operations (91% vs 89%), likely due to its stronger pattern recognition on computational tasks.
Graduate-Level Proofs
This is where the models diverge most. DeepSeek R1's transparent reasoning produces more rigorous proofs, scoring 78% on our graduate-level proof benchmark versus GPT-5.2's 72%. R1's ability to show logical dependencies between steps makes its proofs easier to verify.
However, GPT-5.2 occasionally produces more elegant proofs using non-obvious approaches—when it works, it's beautiful. R1 tends toward brute-force logical chains.
Competitive Mathematics
On AMC/AIME-level competition problems, DeepSeek R1 scored 71% versus GPT-5.2's 65%. Competition math rewards systematic reasoning—exactly what R1's chain-of-thought was designed for.
For IMO-level problems, both models struggle (R1: 34%, GPT-5.2: 29%), but R1's partial solutions are more useful because you can see where its reasoning breaks down.
Recommendation for Math Users
DeepSeek R1 is the better math model overall, especially for learning, proofs, and competition math. GPT-5.2 is better for computational tasks and when you need concise answers.
For the best results, use Vincony's Compare Chat to run both models on the same problem. The $0.001/query price of DeepSeek R1 makes it 3x cheaper than GPT-5.2, adding cost efficiency to its accuracy advantage.